A review of air disturbance technology for plant frost protection

Hu, Yongguang; Asante, Eric Amoah; Lu, Yongzong; Mahmood, Ashraf; Buttar, Noman Ali; Yuan, Shuxia

doi:10.25165/j.ijabe.20181103.3172

Cited by 21 publications

(28 citation statements)

References 42 publications

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“…Advection frosts happened frequently happen in a cold and windy weather, which is associated with large-scale and strong cold air with the temperature below 0 • C. Radiation frosts are caused by the radiant energy loss from the atmosphere, soil and plant ecosystem in the clear, no-wind nights. It will cause peculiar temperature inversions phenomenon [24,25]. Air temperature and air relative humidity are the most vital micrometeorological factors for the formation of the radiation frost [26].…”

Section: Resultsmentioning

confidence: 99%

“…There is a functional relationship between humidity and air temperature, they also changed with the heights with a polynomial function. Canopy of the plant is the most sensitive part for the freezing injury and frost damage [2,24,29]. Plant canopies intercept energy radiated from the land surface to the atmosphere and changes the air flow capacity as well.…”

Section: Resultsmentioning

confidence: 99%

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Prediction of Radiation Frost Using Support Vector Machines Based on Micrometeorological Data

et al. 2019

Applied Sciences

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Radiation frost happens frequently in the Yangtze River Delta region, which causes high economic loss in agriculture industry. It occurs because of heat losses from the atmosphere, plant and soil in the form of radiant energy, which is strongly associated with the micrometeorological characteristics. Multidimensional and nonlinear micrometeorological data enhances the difficulty in predicting the radiation frost. Support vector machines (SVMs), a type of algorithms, can be supervised learning which widely be employed for classification or regression problems in research of precision agriculture. This paper is the first attempt of using SVMs to build prediction models for radiation frost. Thirty-two kinds of micrometeorological parameters, such as daily mean temperature at six heights (Tmean0.5, Tmean1.5, Tmean2.0, Tmean3.0, Tmean4.5 and Tmean6.0), daily maximum and minimum temperatures at six heights (Tmax0.5, Tmax1.5, Tmax2.0, Tmax3.0, Tmax4.5 and Tmax6.0, and Tmin0.5, Tmin1.5, Tmin2.0, Tmin3.0, Tmin4.5 and Tmin6.0), daily mean relative humidity at six heights (RH0.5, RH1.5, RH2.0, RH3.0, RH4.5 and RH6.0), net radiation (Rn), downward short-wave radiation (Rsd), downward long-wave radiation (Rld), upward long-wave radiation (Rlu), upward short-wave radiation (Rsu), soil temperature (Tsoil) and soil heat flux (G) and daily average wind speed (u) were collected from November 2016 to July 2018. Six combinations inputs were used as the basis dataset for testing and training. Three types of kernel functions, such as linear kernel, radial basis function kernel and polynomial kernel function were used to develop the SVMs models. Five-fold cross validation was conducted for model fitting on training dataset to alleviate over-fitting and make prediction results more reliable. The results showed that an SVM with the radial basis function kernel (SVM-BRF) model with all the 32 micrometeorological data obtained high prediction accuracy in training and testing sets. When the single type of data (temperature, humidity and radiation data) was used for the SVM without any functions, prediction accuracy was better than that with functions. The SVM-BRF model had the best prediction accuracy when using the multidimensional and nonlinear micrometeorological data. Considering the complexity level of the model and the accuracy of prediction, micrometeorological data at the canopy height with the SVM-BRF model has been recommended for radiation frost prediction in Yangtze River Delta and probably could be applied in elsewhere with the similar terrains and micro-climates.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Prediction of Radiation Frost Using Support Vector Machines Based on Micrometeorological Data

et al. 2019

Applied Sciences

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“…Such real-time observations could also improve frost and freeze warnings and mitigation response for growers. For instance, Hu et al (2018) reviewed how air disturbance technology (e.g., tower-mounted wind machines) could offer significant frost protection over sensitive orchards during periods of inversions.…”

Section: B Agricultural Sectormentioning

confidence: 99%

Potential Socioeconomic and Environmental Benefits and Beneficiaries of UAS Atmospheric Profiles from a 3D Mesonet

Fiebrich

Ziolkowska

Chilson

et al. 2021

Weather, Climate, and Society

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In recent years, technological developments in engineering and meteorology have provided the opportunity to introduce innovative extensions to traditional surface mesonets through the application of UAS (unmanned aircraft systems). This new approach of measuring vertical profiles of weather variables by means of UAS in the atmospheric boundary layer, in addition to surface stations, has been termed a 3D mesonet.Technological innovations of a potential 3D mesonet have recently been described in the literature. However, a broader question remains about potential socio-economic and environmental benefits and beneficiaries of this new extension. Given that the concept of a 3D mesonet is a new idea, studies about socio-economic and environmental advantages of this network (compared to traditional mesonets) do not appear to exist in the peer reviewed literature.This paper aims to fill this gap by providing a first perspective on potential benefits and ripple effects of a 3D mesonet, addressing both the added-value and prevented losses in specific sectoral applications and for different groups. A better understanding of qualitative economic aspects related to a 3D mesonet can facilitate future developments of this technology for more cost-effective applications and to mitigate environmental challenges in more efficient ways.

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“…Following the historic “false spring” of 2012, agricultural researchers and extension specialists have developed potential adaptations to avoid similar losses in the future. Crop treatments in development or use include preventing early bud break by using mist cooling or frost fans in fields to delay bud break, or keeping trees warm when frost is predicted (Hu et al, ; Rijal, ). Each of these potential adaptations has implications for ecosystem health, including water use, energy consumption, smoke emissions, or herbicide use, which each also have feedbacks to climate change.…”

Section: Case Studiesmentioning

confidence: 99%

Winter Weather Whiplash: Impacts of Meteorological Events Misaligned With Natural and Human Systems in Seasonally Snow‐Covered Regions

et al. 2019

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Weather whiplash" is a colloquial phrase for describing an extreme event that includes shifts between two opposing weather conditions. Prior media coverage and research on these types of extremes have largely ignored winter weather events. However, rapid swings in winter weather can result in crossing from frozen to unfrozen conditions, or vice versa; thus, the potential impact of these types of events on coupled human and natural systems may be large. Given rapidly changing winter conditions in seasonally snow-covered regions, there is a pressing need for a deeper understanding of such events and the extent of their impacts to minimize their risks. Here we introduce the concept of winter weather whiplash, defined as a class of extreme event in which a collision of unexpected conditions produces a forceful, rapid, back-and-forth change in winter weather that induces an outsized impact on coupled human and natural systems. Using a series of case studies, we demonstrate that the effects of winter weather whiplash events depend on the natural and human context in which they occur, and discuss how these events may result in the restructuring of social and ecological systems. We use the long-term hydrometeorological record at the Hubbard Brook Experimental Forest in New Hampshire, USA to demonstrate quantitative methods for delineating winter weather whiplash events and their biophysical impacts. Ultimately, we argue that robust conceptual and quantitative frameworks for understanding winter weather whiplash events will contribute to the ways in which we mitigate and adapt to winter climate change in vulnerable regions. Plain Language SummaryWeather whiplash is a term used by researchers and the media to describe wild and rapid shifts in weather conditions. Here we investigate "winter weather whiplash" events, which are characterized by weather conditions swinging from frozen to unfrozen (or vice versa). These events have important consequences for ecosystems and communities, especially when they occur at unusual times of the year. Impacts of these events include tree damage, flooding, electrical outages, and crop damage. We use a series of case studies to explore the impacts of these events and analyze a long-term data set to demonstrate how they might be detected from weather data. Understanding winter weather whiplash events will help decision makers and planners adapt and mitigate these events in the future.

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A review of air disturbance technology for plant frost protection

Cited by 21 publications

References 42 publications

Prediction of Radiation Frost Using Support Vector Machines Based on Micrometeorological Data

Prediction of Radiation Frost Using Support Vector Machines Based on Micrometeorological Data

Potential Socioeconomic and Environmental Benefits and Beneficiaries of UAS Atmospheric Profiles from a 3D Mesonet

Winter Weather Whiplash: Impacts of Meteorological Events Misaligned With Natural and Human Systems in Seasonally Snow‐Covered Regions

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